Tag: Books

I think this might be the UK version of the cover design; much more preferable than the US version.

This summer I started reading David Spiegelhalter’s book, The Art of Statistics, but never got beyond the second chapter. I also had plans to learn d3 and do a personal visualization project. So much for plans. School ended and then I got bombarded with work.

Summer. Poof. Gone.

So now that I’m back at UM, I started reading The Art of Statistics, again…from the beginning. If anyone asked me about it, I would simply say that I don’t know much about it. For most of my twenty-some working years, my answer was accepted … until now.

Should, should, should…

Should designers learn to code?

Ever since I started designing websites the question, “Should designers learn to code” has been a never-ending debate. I used to think, Nah, leave it to the experts. Focus on your strengths—design! It was the easy answer; the one I, as well as many of my designer friends wanted to hear. Yet now, as a student in a STEAM program, I can say with certainty that a designer who can code has superpowers.

I’m trying to use a method of highlighting where yellow seems important and green are terms I need to learn and look up more if I don’t understand.

Should designers learn statistics?

I don’t think there is a “Pass Go” card for statistics if you want to practice data visualization ethically, truthfully, professionally. Of course you don’t need to be a statistician but as I’m learning, it sure helps you understand data. It helps you ask questions and more questions. I am convinced more than ever that as we move into this Fourth Industrial Revolution, citizens must have at least some amount of data literacy. Spiegelhalter says:

More data means that we need to be even more aware of what the evidence is actually worth.

David Spiegelhalter, Author, The Art of Statistics: Learning from Data

So yes, designers should learn statistics and The Art of Statistics is a great way to start. The format is wonderful because Spiegelhalter asks a question at the beginning of each “lesson”. He weaves jargon, concepts, history and process into stories from data which are stories about people and communities. What’s not to love? His writing isn’t dry. It is conversational, sometimes funny and always approachable. It is void of academic uppity.

Learning about distribution and logarithmic scale.

I decided to try a different approach to learning based on another book I’m also reading, Make it Stick. It’s a great book for teachers and students about learning. One takeaway was to break down anything you are learning into a series of steps or connections.

An excellent book for students and teachers.

That may seem obvious but apparently I went through life with with a different method for learning (memorization). With that in mind, I downloaded an upgrade to MindNode to try it out again (Thank you Qinyu for the reminder) to see if this process of creating a structure, a network, a series of relationships would help lodge some of these new terms and concepts into my long term memory. I’m also hoping this process makes learning statistics more enjoyable. So far, so good. (yay, me)

If you’re curious, you can click on the image or this link to see a better view of what I’ve started.

I’ve created mind maps on paper but what I like about MindNode so far is that I can move networks around easy peasy. The more I read, the more I process, I can go back and adjust as things become more clear.

Networks, Relationships, Connections

As a side note, I’m also learning how to design for artificial intelligence (or is it with?) and what has been interesting these past few weeks are how statistics, psychology, data visualization, artificial intelligence, and human-centered design are moving together and intersecting at various points as I process everything I am learning. The work I do (currently literature reviews) in the UX Lab continues to expose me to related terms and concepts.

As I move through The Art of Statistics, I’m getting early hints that I’ll be learning about analyzing performance for machine learning (regression models, algorithms, prediction). Cool. And recently, I learned about the theory of Connectivism.